{
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "# == Hyperparameter configuration ==\n",
        "\n",
        "# Official scored labels Physionet 2021: https://github.com/physionetchallenges/evaluation-2021/blob/main/dx_mapping_scored.csv\n",
        "\n",
        "# 0 = 426783006 -> sinus rhythm (SR)\n",
        "# 1 = 164889003 -> atrial fibrillation (AF)\n",
        "# 2 = 164890007 -> atrial flutter (AFL)\n",
        "# 3 = 284470004 or 63593006 -> premature atrial contraction (PAC) or supraventricular premature beats (SVPB)\n",
        "# 4 = 427172004 or 17338001 -> premature ventricular contractions (PVC), ventricular premature beats (VPB)\n",
        "# 5 = 6374002 -> bundle branch block (BBB)\n",
        "# 6 = 426627000 -> bradycardia (Brady)\n",
        "# 7 = 733534002 or 164909002 -> complete left bundle branch block (CLBBB), left bundle branch block (LBBB)\n",
        "# 8 = 713427006 or 59118001 -> complete right bundle branch block (CRBBB), right bundle branch block (RBBB)\n",
        "# 9 = 270492004 -> 1st degree av block (IAVB)\n",
        "# 10 = 713426002 -> incomplete right bundle branch block (IRBBB)\n",
        "# 11 = 39732003 -> left axis deviation (LAD)\n",
        "# 12 = 445118002 -> left anterior fascicular block (LAnFB)\n",
        "# 13 = 251146004 -> low qrs voltages (LQRSV)\n",
        "# 14 = 698252002 -> nonspecific intraventricular conduction disorder (NSIVCB)\n",
        "# 15 = 10370003 -> pacing rhythm (PR)\n",
        "# 16 = 365413008 -> poor R wave Progression (PRWP)\n",
        "# 17 = 164947007 -> prolonged pr interval (LPR)\n",
        "# 18 = 111975006 -> prolonged qt interval (LQT)\n",
        "# 19 = 164917005 -> qwave abnormal (QAb)\n",
        "# 20 = 47665007 -> right axis deviation (RAD)\n",
        "# 21 = 427393009 -> sinus arrhythmia (SA)\n",
        "# 22 = 426177001 -> sinus bradycardia (SB)\n",
        "# 23 = 427084000 -> sinus tachycardia (STach)\n",
        "# 24 = 164934002 -> t wave abnormal (TAb)\n",
        "# 25 = 59931005 -> t wave inversion (TInv)\n",
        "\n",
        "VALID_LABELS = set(\n",
        "    [\n",
        "        \"164889003\",\n",
        "        \"164890007\",\n",
        "        \"6374002\",\n",
        "        \"426627000\",\n",
        "        \"733534002\",\n",
        "        \"713427006\",\n",
        "        \"270492004\",\n",
        "        \"713426002\",\n",
        "        \"39732003\",\n",
        "        \"445118002\",\n",
        "        \"164909002\",\n",
        "        \"251146004\",\n",
        "        \"698252002\",\n",
        "        \"426783006\",\n",
        "        \"284470004\",\n",
        "        \"10370003\",\n",
        "        \"365413008\",\n",
        "        \"427172004\",\n",
        "        \"164947007\",\n",
        "        \"111975006\",\n",
        "        \"164917005\",\n",
        "        \"47665007\",\n",
        "        \"59118001\",\n",
        "        \"427393009\",\n",
        "        \"426177001\",\n",
        "        \"427084000\",\n",
        "        \"63593006\",\n",
        "        \"164934002\",\n",
        "        \"59931005\",\n",
        "        \"17338001\",\n",
        "    ]\n",
        ")\n",
        "# VALID_LABELS = set([\"426783006\", \"164889003\", \"164890007\", \"284470004\", \"427172004\"]) # SR, AF, AFL, PAC, PVC\n",
        "NUM_CLASSES =  26\n",
        "\n",
        "CLASS_BALANCE = 2000 # imblearn undersampling & SMOTE oversampling\n",
        "TEST_BALANCE = 100 # imblearn undersampling\n",
        "TRAIN_TEST_SPLIT = 0.8\n",
        "VALIDATION_SPLIT = 0.25\n",
        "\n",
        "EPOCHS = 500\n",
        "LEARNING_RATE = 0.001\n",
        "BATCH_SIZE = 100"
      ],
      "metadata": {
        "id": "UPiaiebnCYov"
      },
      "execution_count": 165,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# == Check if GPU is available ==\n",
        "\n",
        "!nvidia-smi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "68ZDikRKCZ6_",
        "outputId": "1f4d5391-ff02-45db-8a55-86a68780b30c"
      },
      "execution_count": 166,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sun Jun 16 19:38:17 2024       \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 535.104.05             Driver Version: 535.104.05   CUDA Version: 12.2     |\n",
            "|-----------------------------------------+----------------------+----------------------+\n",
            "| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                                         |                      |               MIG M. |\n",
            "|=========================================+======================+======================|\n",
            "|   0  Tesla T4                       Off | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   37C    P8               9W /  70W |      0MiB / 15360MiB |      0%      Default |\n",
            "|                                         |                      |                  N/A |\n",
            "+-----------------------------------------+----------------------+----------------------+\n",
            "                                                                                         \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| Processes:                                                                            |\n",
            "|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\n",
            "|        ID   ID                                                             Usage      |\n",
            "|=======================================================================================|\n",
            "|  No running processes found                                                           |\n",
            "+---------------------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 167,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fEnUK8J5zqBe",
        "outputId": "39edd0b3-9cca-4e4a-ffa7-9263067cad03"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
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            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow) (0.4.0)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow) (4.9)\n",
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            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard<2.16,>=2.15->tensorflow) (3.7)\n",
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            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<2,>=0.5->tensorboard<2.16,>=2.15->tensorflow) (3.2.2)\n",
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            "Requirement already satisfied: imblearn in /usr/local/lib/python3.10/dist-packages (0.0)\n",
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            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n",
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            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn->imblearn) (1.4.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn->imblearn) (3.5.0)\n"
          ]
        }
      ],
      "source": [
        "# == Install requirements ==\n",
        "\n",
        "!pip install google-colab\n",
        "!pip install tensorflow keras numpy\n",
        "!pip install h5py tqdm\n",
        "!pip install pandas scipy imblearn"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 168,
      "metadata": {
        "id": "UD4mNESiD965"
      },
      "outputs": [],
      "source": [
        "# == Import requirements ==\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "from google.colab import drive\n",
        "import os\n",
        "import h5py\n",
        "from tqdm import tqdm\n",
        "import pandas as pd\n",
        "from collections import Counter\n",
        "\n",
        "import random\n",
        "import scipy\n",
        "from scipy.signal import butter, lfilter\n",
        "from sklearn.model_selection import KFold\n",
        "from imblearn.over_sampling import SMOTE\n",
        "from imblearn.pipeline import Pipeline\n",
        "from imblearn.under_sampling import RandomUnderSampler\n",
        "from imblearn.over_sampling import RandomOverSampler\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras import regularizers\n",
        "import numpy as np\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Preprocess functions\n",
        "\n",
        "def pad_or_truncate_ecg(ecg: list, max_samples: int) -> list:\n",
        "    padded_or_truncated_ecg = ecg[:max_samples] + [0] * (max_samples - len(ecg))\n",
        "    return list(padded_or_truncated_ecg)\n",
        "\n",
        "def resample_ecg(ecg: list, resample: int):\n",
        "    new_ecg = scipy.signal.resample(ecg, resample, t=None, axis=0, window=None, domain=\"time\")\n",
        "    return list(new_ecg)\n",
        "\n",
        "def normalize_to_minus11(ecg: list):\n",
        "    max_val = max(ecg)\n",
        "    min_val = min(ecg)\n",
        "    # Handle the case where max_val and min_val are the same (to avoid division by zero)\n",
        "    if max_val == min_val:\n",
        "        return [0 for _ in ecg]\n",
        "    normalized_values = [2 * (x - min_val) / (max_val - min_val) - 1 for x in ecg]\n",
        "    return list(normalized_values)\n",
        "\n",
        "def butter_bandpass(lowcut, highcut, fs, order=4):\n",
        "    nyquist = 0.5 * fs\n",
        "    low = lowcut / nyquist\n",
        "    high = highcut / nyquist\n",
        "    b, a = butter(order, [low, high], btype=\"band\")\n",
        "    return b, a\n",
        "\n",
        "def butter_bandpass_filter(ecg: list, lowcut: float, highcut: float, sampling_rate: int, order: int =4):\n",
        "    b, a = butter_bandpass(lowcut, highcut, sampling_rate, order=order)\n",
        "    y = lfilter(b, a, ecg)\n",
        "    return list(y)\n",
        "\n",
        "def split_list_into_n_sublists(lst, n):\n",
        "    k, m = divmod(len(lst), n)\n",
        "    return [lst[i * k + min(i, m):(i + 1) * k + min(i + 1, m)] for i in range(n)]"
      ],
      "metadata": {
        "id": "rcryqS0FDqBV"
      },
      "execution_count": 169,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 170,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LZTY2UE1Mhlj",
        "outputId": "88daa83d-0c2d-46f7-ada0-66a54f561c86"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n",
            "codes_SNOMED.csv  physionet2017_references.csv\tphysionet2021_references.csv\n",
            "physionet2017.h5  physionet2021.h5\t\tprepared\n"
          ]
        }
      ],
      "source": [
        "# == Mount drive ==\n",
        "\n",
        "# https://drive.google.com/drive/folders/1L_gOMrkygu2N0k97COYuVrmE-AwEEMoQ\n",
        "\n",
        "drive.mount('/content/drive')\n",
        "path = \"/content/drive/My Drive/Master Thesis/Datasets\"\n",
        "!ls \"/content/drive/My Drive/Master Thesis/Datasets\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 171,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ejP3dsia8Wdq",
        "outputId": "51dd0705-fb39-4dfc-d902-a33ae87666e3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Preprocess ECGs: 100%|██████████| 129357/129357 [21:15<00:00, 101.44it/s]\n",
            "Load ECG data: 100%|██████████| 81960/81960 [03:53<00:00, 350.59it/s]\n",
            "Load ECG labels:  98%|█████████▊| 86790/88252 [00:04<00:00, 20036.86it/s]"
          ]
        }
      ],
      "source": [
        "# == Load all Physionet2021 ECGs and their IDs to a dictionary X_dict ==\n",
        "\n",
        "X_dict = {}\n",
        "Y_dict = {}\n",
        "\n",
        "h5file = h5py.File(os.path.join(path, \"prepared/physionet2021_scoredLabels.h5\"), \"r\")\n",
        "IDs = list(h5file.keys())\n",
        "pbar = tqdm(total=len(IDs), desc=\"Load ECG data\", position=0, leave=True)\n",
        "for key in IDs:\n",
        "    ecg = []\n",
        "    for lead_index, _ in enumerate(h5file[key][:2]):\n",
        "        ecg.append(list(h5file[key][lead_index]))\n",
        "    X_dict[key] = list(ecg)\n",
        "    pbar.update(1)\n",
        "\n",
        "# == Load all labels and their IDs to a dictionary Y_dict (some ECGs can have multiple labels) ==\n",
        "\n",
        "labels_df = pd.read_csv(os.path.join(path, \"physionet2021_references.csv\"), sep=\";\")\n",
        "pbar = tqdm(total=len(labels_df), desc=\"Load ECG labels\", position=0, leave=True)\n",
        "for _, row in labels_df.iterrows():\n",
        "    Y_dict[row[\"id\"]] = row[\"labels\"].split(\",\")\n",
        "    pbar.update(1)\n",
        "\n",
        "# del IDs, h5file"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 172,
      "metadata": {
        "id": "UkTDLTJy9ku0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c9a38122-1d40-4e05-dfe5-5b8953da17e8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Map labels to ECGs: 100%|██████████| 88252/88252 [00:00<00:00, 578804.22it/s]\n"
          ]
        }
      ],
      "source": [
        "# == Map scored labels to ECGs and create three lists (X: ECGs, Y: labels, Z: IDs) ==\n",
        "\n",
        "X = []\n",
        "Y = []\n",
        "Z = []\n",
        "\n",
        "for patient_id in tqdm(Y_dict, desc=\"Map labels to ECGs\", position=0, leave=True):\n",
        "    for label in Y_dict[patient_id]:\n",
        "        try:\n",
        "            if label in VALID_LABELS:\n",
        "                X.append(X_dict[patient_id])\n",
        "                Y.append(str(label))\n",
        "                Z.append(str(patient_id))\n",
        "        except:\n",
        "            pass\n",
        "\n",
        "# del X_dict, Y_dict"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 173,
      "metadata": {
        "id": "y2eES-Y84ksK"
      },
      "outputs": [],
      "source": [
        "# == Map labels to numerical values ==\n",
        "\n",
        "# Official scored labels Physionet 2021: https://github.com/physionetchallenges/evaluation-2021/blob/main/dx_mapping_scored.csv\n",
        "\n",
        "Y = [0 if x == \"426783006\" else x for x in Y] # sinus rhythm (SR)\n",
        "Y = [1 if x == \"164889003\" else x for x in Y] # atrial fibrillation (AF)\n",
        "Y = [2 if x == \"164890007\" else x for x in Y] # atrial flutter (AFL)\n",
        "Y = [3 if x == \"284470004\" or x == \"63593006\" else x for x in Y] # premature atrial contraction (PAC), supraventricular premature beats (SVPB)\n",
        "Y = [4 if x == \"427172004\" or x == \"17338001\" else x for x in Y] # premature ventricular contractions (PVC), ventricular premature beats (VPB)\n",
        "Y = [5 if x == \"6374002\" else x for x in Y] # bundle branch block (BBB)\n",
        "Y = [6 if x == \"426627000\" else x for x in Y] # bradycardia (Brady)\n",
        "Y = [7 if x == \"733534002\" or x == \"164909002\" else x for x in Y] # complete left bundle branch block (CLBBB), left bundle branch block (LBBB)\n",
        "Y = [8 if x == \"713427006\" or x == \"59118001\" else x for x in Y] # complete right bundle branch block (CRBBB), right bundle branch block (RBBB)\n",
        "Y = [9 if x == \"270492004\" else x for x in Y] # 1st degree av block (IAVB)\n",
        "Y = [10 if x == \"713426002\" else x for x in Y] # incomplete right bundle branch block (IRBBB)\n",
        "Y = [11 if x == \"39732003\" else x for x in Y] # left axis deviation (LAD)\n",
        "Y = [12 if x == \"445118002\" else x for x in Y] # left anterior fascicular block (LAnFB)\n",
        "Y = [13 if x == \"251146004\" else x for x in Y] # low qrs voltages (LQRSV)\n",
        "Y = [14 if x == \"698252002\" else x for x in Y] # nonspecific intraventricular conduction disorder (NSIVCB)\n",
        "Y = [15 if x == \"10370003\" else x for x in Y] # pacing rhythm (PR)\n",
        "Y = [16 if x == \"365413008\" else x for x in Y] # poor R wave Progression (PRWP)\n",
        "Y = [17 if x == \"164947007\" else x for x in Y] # prolonged pr interval (LPR)\n",
        "Y = [18 if x == \"111975006\" else x for x in Y] # prolonged qt interval (LQT)\n",
        "Y = [19 if x == \"164917005\" else x for x in Y] # qwave abnormal (QAb)\n",
        "Y = [20 if x == \"47665007\" else x for x in Y] # right axis deviation (RAD)\n",
        "Y = [21 if x == \"427393009\" else x for x in Y] # sinus arrhythmia (SA)\n",
        "Y = [22 if x == \"426177001\" else x for x in Y] # sinus bradycardia (SB)\n",
        "Y = [23 if x == \"427084000\" else x for x in Y] # sinus tachycardia (STach)\n",
        "Y = [24 if x == \"164934002\" else x for x in Y] # t wave abnormal (TAb)\n",
        "Y = [25 if x == \"59931005\" else x for x in Y] # t wave inversion (TInv)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 174,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8keoyR_gDMaX",
        "outputId": "9a069d71-9700-41ea-e371-926d95321890"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Load ECG labels: 100%|██████████| 88252/88252 [00:05<00:00, 16223.89it/s]\n",
            "Preprocess ECGs: 100%|█████████▉| 129352/129357 [08:52<00:00, 244.03it/s]"
          ]
        }
      ],
      "source": [
        "# == Preprocess ECGs ==\n",
        "\n",
        "pbar = tqdm(total=len(X), desc=\"Preprocess ECGs\", position=0, leave=True)\n",
        "for index, _ in enumerate(X):\n",
        "    for lead_index, _ in enumerate(X[index]):\n",
        "        X[index][lead_index] = pad_or_truncate_ecg(ecg=list(X[index][lead_index]), max_samples=5000)\n",
        "        X[index][lead_index] = resample_ecg(ecg=X[index][lead_index], resample=2000)\n",
        "        X[index][lead_index] = normalize_to_minus11(ecg=X[index][lead_index])\n",
        "        X[index][lead_index] = butter_bandpass_filter(ecg=X[index][lead_index], lowcut=0.3, highcut=21.0, sampling_rate=200)\n",
        "    pbar.update(1)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# == Split train-test sets by patients if train-test splits files are given ==\n",
        "\n",
        "if os.path.exists(\"trainset_patient_ids.txt\") and os.path.exists(\"testset_patient_ids.txt\"):\n",
        "    f = open(\"trainset_patient_ids.txt\", \"r\", encoding=\"utf-8\")\n",
        "    trainset_patient_ids = f.readlines()\n",
        "    f.close()\n",
        "    f = open(\"testset_patient_ids.txt\", \"r\", encoding=\"utf-8\")\n",
        "    testset_patient_ids = f.readlines()\n",
        "    f.close()\n",
        "    trainset_patient_ids = list(map(lambda x: x.replace(\"\\n\", \"\"), trainset_patient_ids))\n",
        "    testset_patient_ids = list(map(lambda x: x.replace(\"\\n\", \"\"), testset_patient_ids))\n",
        "    trainset_patient_ids = set(trainset_patient_ids)\n",
        "    testset_patient_ids = set(testset_patient_ids)\n",
        "    X_train = []\n",
        "    Y_train = []\n",
        "    Z_train = []\n",
        "    X_test = []\n",
        "    Y_test = []\n",
        "    Z_test = []\n",
        "    for sample in zip(X, Y, Z):\n",
        "        if sample[2] in trainset_patient_ids:\n",
        "            X_train.append(sample[0])\n",
        "            Y_train.append(sample[1])\n",
        "            Z_train.append(sample[2])\n",
        "        elif sample[2] in testset_patient_ids:\n",
        "            X_test.append(sample[0])\n",
        "            Y_test.append(sample[1])\n",
        "            Z_test.append(sample[2])\n",
        "#    del X, Y, Z"
      ],
      "metadata": {
        "id": "VAYphezXWKSx"
      },
      "execution_count": 175,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 176,
      "metadata": {
        "id": "S1t3m5cUio4c"
      },
      "outputs": [],
      "source": [
        "# == Split data train-test sets by patients ==\n",
        "\n",
        "if not os.path.exists(\"trainset_patient_ids.txt\") and not os.path.exists(\"testset_patient_ids.txt\"):\n",
        "    bins = []\n",
        "    for x in range(NUM_CLASSES):\n",
        "        bins.append([])\n",
        "\n",
        "    # Create num_classes bins (1 bin = 1 class)\n",
        "    for sample in zip(X, Y, Z):\n",
        "        bins[sample[1]].append([sample[0], sample[1], sample[2]])\n",
        "\n",
        "    # Create train-test bins (e.g. TRAIN_TEST_SPLIT = 80%-20%)\n",
        "    train_bins = []\n",
        "    test_bins = []\n",
        "    for x in range(NUM_CLASSES):\n",
        "        train_bins.append([])\n",
        "        test_bins.append([])\n",
        "    for index, bin in enumerate(bins):\n",
        "        split_index = int(len(bin) * TRAIN_TEST_SPLIT)\n",
        "        train_bins[index] = bin[:split_index]\n",
        "        test_bins[index] = bin[split_index:]\n",
        "\n",
        "    # Create test set (sort bins by occurence of labels descending before adding to train set because of minor classes and multiple labels and add if patient id is not in train set)\n",
        "    X_test = []\n",
        "    Y_test = []\n",
        "    Z_test = []\n",
        "    id_already_in_x_test = set()\n",
        "    test_bins.sort(key=lambda x: len(x))\n",
        "    for bin in test_bins:\n",
        "        for sample in bin:\n",
        "            if sample[2] not in id_already_in_x_test:\n",
        "                id_already_in_x_test.add(sample[2])\n",
        "                X_test.append(sample[0])\n",
        "                Y_test.append(sample[1])\n",
        "                Z_test.append(sample[2])\n",
        "\n",
        "    # Create train set (sort bins by occurence of labels descending before adding to train set because of minor classes and multiple labels and add if patient id is not in train (split by patients) or test set (multiple labels in test set would bias the evaluation) already)\n",
        "    X_train = []\n",
        "    Y_train = []\n",
        "    Z_train = []\n",
        "    id_already_in_x_train = set()\n",
        "    train_bins.sort(key=lambda x: len(x))\n",
        "    for bin in train_bins:\n",
        "        for sample in bin:\n",
        "            if sample[2] not in id_already_in_x_train and sample[2] not in id_already_in_x_test:\n",
        "                id_already_in_x_train.add(sample[2])\n",
        "                X_train.append(sample[0])\n",
        "                Y_train.append(sample[1])\n",
        "                Z_train.append(sample[2])\n",
        "\n",
        "    # Write train and test patient ids to file\n",
        "    id_already_in_x_train_list = list(id_already_in_x_train)\n",
        "    id_already_in_x_train_list = list(map(lambda x: str(x) + \"\\n\", id_already_in_x_train_list))\n",
        "    id_already_in_x_test_list = list(id_already_in_x_test)\n",
        "    id_already_in_x_test_list = list(map(lambda x: str(x) + \"\\n\", id_already_in_x_test_list))\n",
        "    with open('trainset_patient_ids.txt', 'w', encoding='utf-8') as file:\n",
        "        file.writelines(id_already_in_x_train_list)\n",
        "    with open('testset_patient_ids.txt', 'w', encoding='utf-8') as file:\n",
        "        file.writelines(id_already_in_x_test_list)\n",
        "\n",
        "#    del X, Y, Z"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(Counter(Y_train))\n",
        "print(Counter(Y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "01ZuC0-Nj2NX",
        "outputId": "65e1f4ae-f265-42e9-ef52-33f9a5b5d2d4"
      },
      "execution_count": 177,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Counter({0: 19812, 22: 14985, 24: 7824, 23: 7566, 2: 6699, 11: 4145, 1: 3624, 8: 3595, 21: 3002, 25: 2986, 9: 2558, 3: 2433, 4: 1480, 18: 1457, 19: 1376, 12: 1163, 13: 1154, 10: 1153, 15: 1142, 14: 959, 7: 870, 20: 825, 16: 494, 5: 404, 6: 232, 17: 134})\n",
            "Counter({0: 9159, 22: 3933, 24: 3892, 11: 3486, 23: 2091, 2: 1675, 1: 1631, 8: 1235, 12: 1023, 25: 1003, 9: 976, 3: 832, 14: 809, 21: 788, 10: 704, 19: 700, 7: 619, 4: 458, 20: 455, 18: 450, 13: 445, 15: 338, 17: 258, 16: 144, 5: 118, 6: 63})\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for index, x in enumerate(X_train):\n",
        "    for lead_index, lead in enumerate(X_train[index]):\n",
        "        X_train[index][lead_index] = np.array(X_train[index][lead_index])\n",
        "    X_train[index] = np.array(X_train[index])\n",
        "for index, x in enumerate(X_test):\n",
        "    for lead_index, lead in enumerate(X_test[index]):\n",
        "        X_test[index][lead_index] = np.array(X_test[index][lead_index])\n",
        "    X_test[index] = np.array(X_test[index])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JxNnDb2WHASU",
        "outputId": "f744efec-0b16-4bee-e023-42a44e21fd90"
      },
      "execution_count": 178,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rPreprocess ECGs: 100%|██████████| 129357/129357 [09:11<00:00, 244.03it/s]"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for index, x in enumerate(X_train):\n",
        "    X_train[index] = X_train[index].reshape(4000)\n",
        "for index, x in enumerate(X_test):\n",
        "    X_test[index] = X_test[index].reshape(4000)"
      ],
      "metadata": {
        "id": "Ave4WtAKILiX"
      },
      "execution_count": 179,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# == Balance data ==\n",
        "\n",
        "# Trainset\n",
        "\n",
        "count_train = Counter(Y_train)\n",
        "\n",
        "undersampling_strategy = {}\n",
        "oversampling_strategy = {}\n",
        "for i in count_train:\n",
        "    if count_train[i] > CLASS_BALANCE:\n",
        "        undersampling_strategy[i] = CLASS_BALANCE\n",
        "    elif count_train[i] <= CLASS_BALANCE:\n",
        "        oversampling_strategy[i] = CLASS_BALANCE\n",
        "print(f\"Trainset undersampling_strategy: {undersampling_strategy}\")\n",
        "print(f\"Trainset oversampling_strategy: {oversampling_strategy}\")\n",
        "\n",
        "under = RandomUnderSampler(sampling_strategy=undersampling_strategy)\n",
        "over = RandomOverSampler(sampling_strategy=oversampling_strategy)\n",
        "steps = [(\"u\", under), (\"o\", over)]\n",
        "pipeline = Pipeline(steps=steps)\n",
        "X_train_balanced, Y_train_balanced = pipeline.fit_resample(X_train, Y_train)\n",
        "\n",
        "count_train_balanced = Counter(Y_train_balanced)\n",
        "\n",
        "# Testset\n",
        "\n",
        "count_test = Counter(Y_test)\n",
        "\n",
        "# min_key = min(count_test, key=count_test.get)\n",
        "# min_value = count_test[min_key]\n",
        "\n",
        "undersampling_strategy = {}\n",
        "for i in count_test:\n",
        "    if count_test[i] > TEST_BALANCE:\n",
        "        undersampling_strategy[i] = TEST_BALANCE\n",
        "\n",
        "print(f\"Testset undersampling_strategy: {undersampling_strategy}\")\n",
        "\n",
        "under = RandomUnderSampler(sampling_strategy=undersampling_strategy)\n",
        "steps = [(\"u\", under)]\n",
        "pipeline = Pipeline(steps=steps)\n",
        "X_test_balanced, Y_test_balanced = pipeline.fit_resample(X_test, Y_test)\n",
        "\n",
        "count_test_balanced = Counter(Y_test_balanced)\n",
        "\n",
        "print(f\"Trainset: {count_train}\")\n",
        "print(f\"Testset: {count_test}\")\n",
        "print(f\"Trainset balanced: {count_train_balanced}\")\n",
        "print(f\"Testset balanced: {count_test_balanced}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ejt6gOg7dPMd",
        "outputId": "9bb53fff-0b96-4f6f-fba5-72f9258e36f4"
      },
      "execution_count": 180,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Trainset undersampling_strategy: {0: 2000, 23: 2000, 25: 2000, 24: 2000, 2: 2000, 21: 2000, 9: 2000, 22: 2000, 8: 2000, 11: 2000, 3: 2000, 1: 2000}\n",
            "Trainset oversampling_strategy: {12: 2000, 13: 2000, 16: 2000, 19: 2000, 5: 2000, 18: 2000, 14: 2000, 6: 2000, 4: 2000, 20: 2000, 7: 2000, 15: 2000, 10: 2000, 17: 2000}\n",
            "Testset undersampling_strategy: {2: 100, 8: 100, 24: 100, 5: 100, 25: 100, 12: 100, 19: 100, 16: 100, 7: 100, 10: 100, 20: 100, 11: 100, 14: 100, 15: 100, 13: 100, 21: 100, 18: 100, 4: 100, 23: 100, 0: 100, 3: 100, 17: 100, 22: 100, 9: 100, 1: 100}\n",
            "Trainset: Counter({0: 19812, 22: 14985, 24: 7824, 23: 7566, 2: 6699, 11: 4145, 1: 3624, 8: 3595, 21: 3002, 25: 2986, 9: 2558, 3: 2433, 4: 1480, 18: 1457, 19: 1376, 12: 1163, 13: 1154, 10: 1153, 15: 1142, 14: 959, 7: 870, 20: 825, 16: 494, 5: 404, 6: 232, 17: 134})\n",
            "Testset: Counter({0: 9159, 22: 3933, 24: 3892, 11: 3486, 23: 2091, 2: 1675, 1: 1631, 8: 1235, 12: 1023, 25: 1003, 9: 976, 3: 832, 14: 809, 21: 788, 10: 704, 19: 700, 7: 619, 4: 458, 20: 455, 18: 450, 13: 445, 15: 338, 17: 258, 16: 144, 5: 118, 6: 63})\n",
            "Trainset balanced: Counter({0: 2000, 1: 2000, 2: 2000, 3: 2000, 4: 2000, 5: 2000, 6: 2000, 7: 2000, 8: 2000, 9: 2000, 10: 2000, 11: 2000, 12: 2000, 13: 2000, 14: 2000, 15: 2000, 16: 2000, 17: 2000, 18: 2000, 19: 2000, 20: 2000, 21: 2000, 22: 2000, 23: 2000, 24: 2000, 25: 2000})\n",
            "Testset balanced: Counter({0: 100, 1: 100, 2: 100, 3: 100, 4: 100, 5: 100, 7: 100, 8: 100, 9: 100, 10: 100, 11: 100, 12: 100, 13: 100, 14: 100, 15: 100, 16: 100, 17: 100, 18: 100, 19: 100, 20: 100, 21: 100, 22: 100, 23: 100, 24: 100, 25: 100, 6: 63})\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#for index, x in enumerate(X_train):\n",
        "#    X_train[index] = X_train[index].reshape(2,2000)\n",
        "#for index, x in enumerate(X_train_balanced):\n",
        "#    X_train_balanced[index] = np.array(X_train_balanced[index]).reshape(2,2000)\n",
        "#for index, x in enumerate(X_test):\n",
        "#    X_test[index] = X_test[index].reshape(2,2000)\n",
        "#for index, x in enumerate(X_test_balanced):\n",
        "#    X_test_balanced[index] = np.array(X_test_balanced[index]).reshape(2,2000)"
      ],
      "metadata": {
        "id": "UUhb0yH6fan3"
      },
      "execution_count": 182,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# == Shuffle data, convert to numpy lists and reshape ==\n",
        "\n",
        "# Shuffle data\n",
        "\n",
        "combined = list(zip(X_train, Y_train))\n",
        "random.shuffle(combined)\n",
        "X_train, Y_train = zip(*combined)\n",
        "X_train = list(X_train)\n",
        "Y_train = list(Y_train)\n",
        "\n",
        "combined = list(zip(X_test, Y_test))\n",
        "random.shuffle(combined)\n",
        "X_test, Y_test = zip(*combined)\n",
        "X_test = list(X_test)\n",
        "Y_test = list(Y_test)\n",
        "\n",
        "combined = list(zip(X_train_balanced, Y_train_balanced))\n",
        "random.shuffle(combined)\n",
        "X_train_balanced, Y_train_balanced = zip(*combined)\n",
        "X_train_balanced = list(X_train_balanced)\n",
        "Y_train_balanced = list(Y_train_balanced)\n",
        "\n",
        "combined = list(zip(X_test_balanced, Y_test_balanced))\n",
        "random.shuffle(combined)\n",
        "X_test_balanced, Y_test_balanced = zip(*combined)\n",
        "X_test_balanced = list(X_test_balanced)\n",
        "Y_test_balanced = list(Y_test_balanced)\n",
        "\n",
        "print(f\"Y_train: {Y_train[:50]}\")\n",
        "print(f\"Y_test: {Y_test[:50]}\")\n",
        "print(f\"Y_train_balanced: {Y_train_balanced[:50]}\")\n",
        "print(f\"Y_test_balanced: {Y_test_balanced[:50]}\")\n",
        "\n",
        "# Convert to numpy lists\n",
        "\n",
        "Y_train = np.array(Y_train)\n",
        "Y_test = np.array(Y_test)\n",
        "Y_train_balanced = np.array(Y_train_balanced)\n",
        "Y_test_balanced = np.array(Y_test_balanced)\n",
        "\n",
        "for index, x in enumerate(X_train):\n",
        "    X_train[index] = np.array(x)\n",
        "X_train = np.array(X_train)\n",
        "\n",
        "for index, x in enumerate(X_test):\n",
        "    X_test[index] = np.array(x)\n",
        "X_test = np.array(X_test)\n",
        "\n",
        "for index, x in enumerate(X_train_balanced):\n",
        "    X_train_balanced[index] = np.array(x)\n",
        "X_train_balanced = np.array(X_train_balanced)\n",
        "\n",
        "for index, x in enumerate(X_test_balanced):\n",
        "    X_test_balanced[index] = np.array(x)\n",
        "X_test_balanced = np.array(X_test_balanced)\n",
        "\n",
        "# Reshape\n",
        "\n",
        "# X_train = X_train.reshape((-1, 2000, 1))\n",
        "# X_test = X_test.reshape((-1, 2000, 1))\n",
        "# X_train_balanced = X_train_balanced.reshape((-1, 2000, 1))\n",
        "# X_test_balanced = X_test_balanced.reshape((-1, 2000, 1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oH1on-V7RJ1s",
        "outputId": "a25d4eef-6af9-4806-eb7a-e4f06c48a64a"
      },
      "execution_count": 183,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Y_train: [23, 22, 0, 22, 23, 0, 23, 22, 21, 18, 0, 8, 0, 22, 2, 8, 24, 8, 0, 24, 0, 0, 21, 24, 2, 9, 0, 22, 24, 24, 22, 4, 0, 0, 8, 0, 23, 22, 22, 3, 0, 23, 23, 0, 9, 11, 22, 11, 21, 3]\n",
            "Y_test: [0, 24, 24, 0, 0, 8, 14, 22, 23, 23, 0, 24, 0, 0, 11, 9, 1, 0, 22, 24, 9, 0, 11, 0, 24, 11, 23, 0, 0, 11, 0, 0, 0, 0, 25, 11, 23, 0, 21, 19, 22, 1, 19, 0, 25, 12, 17, 0, 0, 22]\n",
            "Y_train_balanced: [20, 4, 2, 11, 5, 17, 12, 8, 0, 6, 14, 25, 22, 19, 16, 15, 24, 17, 6, 16, 9, 11, 24, 3, 7, 20, 4, 8, 9, 12, 0, 12, 6, 1, 4, 0, 19, 1, 20, 18, 14, 6, 3, 16, 20, 13, 25, 21, 21, 3]\n",
            "Y_test_balanced: [19, 25, 16, 23, 4, 25, 15, 12, 0, 9, 5, 0, 1, 25, 9, 2, 13, 12, 24, 22, 19, 17, 16, 21, 19, 1, 25, 2, 11, 15, 17, 6, 4, 22, 7, 13, 10, 11, 14, 15, 5, 4, 16, 14, 4, 0, 5, 16, 22, 19]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 201,
      "metadata": {
        "id": "eEsV9r0EjSTw"
      },
      "outputs": [],
      "source": [
        "# == A-B-testing models ==\n",
        "\n",
        "# Model A: Residual_CNN_1lead\n",
        "def conv(i, filters=16, kernel_size=9, strides=1):\n",
        "    i = keras.layers.Conv1D(\n",
        "        filters=filters, kernel_size=kernel_size, strides=strides, padding=\"same\"\n",
        "    )(i)\n",
        "    i = keras.layers.BatchNormalization()(i)\n",
        "    i = keras.layers.LeakyReLU()(i)\n",
        "    i = keras.layers.SpatialDropout1D(0.1)(i)\n",
        "    return i\n",
        "def residual_unit(x, filters, layers=3):\n",
        "    inp = x\n",
        "    for i in range(layers):\n",
        "        x = conv(x, filters)\n",
        "    return keras.layers.add([x, inp])\n",
        "def conv_block(x, filters, strides):\n",
        "    x = conv(x, filters)\n",
        "    x = residual_unit(x, filters)\n",
        "    if strides > 1:\n",
        "        x = keras.layers.AveragePooling1D(strides, strides)(x)\n",
        "    return x\n",
        "def build_model_A(input_shape, num_classes):\n",
        "    inp = keras.layers.Input(input_shape)\n",
        "    x = inp\n",
        "    x = conv_block(x, 16, 4)\n",
        "    x = conv_block(x, 16, 4)\n",
        "    x = conv_block(x, 32, 4)\n",
        "    x = conv_block(x, 32, 4)\n",
        "    x = keras.layers.Masking(mask_value=0)(x)\n",
        "    x = keras.layers.GRU(128, recurrent_dropout=0.1)(x)\n",
        "    x = keras.layers.Dense(num_classes, activation=\"softmax\")(x)\n",
        "    model = keras.models.Model(inp, x)\n",
        "    return model\n",
        "\n",
        "# Model B: CNN_Transformer_1lead\n",
        "def build_model_B(num_classes, input_shape):\n",
        "    # input_shape = (2000, 1)  # Each sample has 2000 timesteps and 1 feature per timestep\n",
        "    input_layer = keras.layers.Input(input_shape)\n",
        "\n",
        "    # Masking for padded/truncated data\n",
        "    i = keras.layers.Masking(mask_value=0)(input_layer)\n",
        "    # Conv1\n",
        "    i = keras.layers.Conv1D(filters=16, kernel_size=9, strides=1, padding=\"same\")(i)\n",
        "    i = keras.layers.BatchNormalization()(i)\n",
        "    i = keras.layers.ReLU()(i)\n",
        "    i = keras.layers.SpatialDropout1D(0.1)(i)\n",
        "    i = keras.layers.AveragePooling1D(2)(i)\n",
        "    # Conv2\n",
        "    i = keras.layers.Conv1D(filters=32, kernel_size=9, strides=1, padding=\"same\")(i)\n",
        "    i = keras.layers.BatchNormalization()(i)\n",
        "    i = keras.layers.ReLU()(i)\n",
        "    i = keras.layers.SpatialDropout1D(0.1)(i)\n",
        "    i = keras.layers.AveragePooling1D(2)(i)\n",
        "    # Conv3\n",
        "    i = keras.layers.Conv1D(filters=64, kernel_size=9, strides=1, padding=\"same\")(i)\n",
        "    i = keras.layers.BatchNormalization()(i)\n",
        "    i = keras.layers.ReLU()(i)\n",
        "    i = keras.layers.SpatialDropout1D(0.1)(i)\n",
        "    i = keras.layers.AveragePooling1D(2)(i)\n",
        "    # Channel Average Pooling and Reshaping\n",
        "    i = keras.layers.GlobalAveragePooling1D(data_format=\"channels_first\")(i)\n",
        "    i = keras.layers.Reshape((5, 50))(i)\n",
        "    # Encoder block/Attention mechanisms\n",
        "    i = keras.layers.MultiHeadAttention(num_heads=10, key_dim=5, dropout=0.3)(i, i)\n",
        "    # i = keras.layers.MultiHeadAttention(num_heads=8, key_dim=50, dropout=0.3)(i, i)\n",
        "    # i = keras.layers.MultiHeadAttention(num_heads=8, key_dim=50, dropout=0.3)(i, i)\n",
        "    # Flatten\n",
        "    i = keras.layers.Flatten()(i)\n",
        "    # Feedforward Softmax\n",
        "    i = keras.layers.Dense(num_classes, activation=\"softmax\")(i)\n",
        "    return keras.models.Model(inputs=input_layer, outputs=i)\n",
        "\n",
        "# Model C: vanilla_Transformer_1lead\n",
        "def transformer_encoder(input, input_shape, num_heads, key_dim, ff_dim, dropout):\n",
        "    # Multi-Head Attention\n",
        "    x = keras.layers.MultiHeadAttention(num_heads=num_heads, key_dim=key_dim, dropout=dropout, kernel_regularizer=regularizers.l2(0.001))(input, input)\n",
        "    # Add & Normalize\n",
        "    res = x + input\n",
        "    x = keras.layers.LayerNormalization(epsilon=1e-6)(res)\n",
        "    # Feed-Forward Layer\n",
        "    x = keras.layers.Flatten(input_shape=input_shape)(x)\n",
        "    x = keras.layers.Dense(units=ff_dim, activation='relu', kernel_regularizer=regularizers.l2(0.001))(x)\n",
        "    x = keras.layers.Dense(input_shape[0] * input_shape[1], kernel_regularizer=regularizers.l2(0.001))(x)\n",
        "    x = keras.layers.Reshape(input_shape)(x)\n",
        "    x = keras.layers.Dropout(rate=dropout)(x)\n",
        "    # Add & Normalize\n",
        "    x = x + res\n",
        "    x = keras.layers.LayerNormalization(epsilon=1e-6)(x)\n",
        "    return x\n",
        "\n",
        "def build_model_C(num_classes, input_shape, num_encoder_blocks, num_heads, key_dim, ff_dim, dropout):\n",
        "    inputs = keras.Input(shape=input_shape)\n",
        "    x = inputs\n",
        "    for _ in range(num_encoder_blocks):\n",
        "        x = transformer_encoder(x, input_shape, key_dim, num_heads, ff_dim, dropout)\n",
        "    x = keras.layers.Flatten(input_shape=input_shape)(x)\n",
        "    outputs = keras.layers.Dense(units=num_classes, activation='softmax')(x)\n",
        "    return keras.Model(inputs, outputs)\n",
        "\n",
        "# Model D: channel_attention_1lead\n",
        "from tensorflow.keras.layers import Input, Conv1D, GlobalAveragePooling1D, GlobalMaxPooling1D, Dense, Multiply, Add, Activation\n",
        "from tensorflow.keras.models import Model\n",
        "\n",
        "def channel_attention(input_feature, ratio=8):\n",
        "    channel = input_feature.shape[-1]\n",
        "\n",
        "    shared_layer_one = Dense(channel//ratio, activation='relu', kernel_initializer='he_normal', use_bias=True, bias_initializer='zeros')\n",
        "    shared_layer_two = Dense(channel, kernel_initializer='he_normal', use_bias=True, bias_initializer='zeros')\n",
        "\n",
        "    avg_pool = GlobalAveragePooling1D()(input_feature)\n",
        "    avg_pool = shared_layer_one(avg_pool)\n",
        "    avg_pool = shared_layer_two(avg_pool)\n",
        "\n",
        "    max_pool = GlobalMaxPooling1D()(input_feature)\n",
        "    max_pool = shared_layer_one(max_pool)\n",
        "    max_pool = shared_layer_two(max_pool)\n",
        "\n",
        "    cbam_feature = Add()([avg_pool, max_pool])\n",
        "    cbam_feature = Activation('sigmoid')(cbam_feature)\n",
        "\n",
        "    return Multiply()([input_feature, cbam_feature])\n",
        "\n",
        "def build_model_D(input_shape, num_classes):\n",
        "    inputs = Input(shape=input_shape)\n",
        "\n",
        "    x = Conv1D(64, kernel_size=3, padding='same', activation='relu')(inputs)\n",
        "    x = channel_attention(x)\n",
        "\n",
        "    x = Conv1D(128, kernel_size=3, padding='same', activation='relu')(x)\n",
        "    x = channel_attention(x)\n",
        "\n",
        "    x = Conv1D(256, kernel_size=3, padding='same', activation='relu')(x)\n",
        "    x = channel_attention(x)\n",
        "\n",
        "    x = GlobalAveragePooling1D()(x)\n",
        "    outputs = Dense(num_classes, activation='softmax')(x)\n",
        "\n",
        "    model = Model(inputs, outputs)\n",
        "    return model\n",
        "\n",
        "# Model E: Residual_CNN_2lead\n",
        "\n",
        "import keras\n",
        "from keras.layers import Conv2D, BatchNormalization, LeakyReLU, SpatialDropout2D, AveragePooling2D, GRU, Dense, Input, Masking, add\n",
        "\n",
        "# 2D version of conv layer\n",
        "def conv(i, filters=16, kernel_size=(2, 9), strides=(1, 1)):\n",
        "    i = Conv2D(filters=filters, kernel_size=kernel_size, strides=strides, padding=\"same\")(i)\n",
        "    i = BatchNormalization()(i)\n",
        "    i = LeakyReLU()(i)\n",
        "    i = SpatialDropout2D(0.1)(i)\n",
        "    return i\n",
        "\n",
        "# 2D version of residual unit\n",
        "def residual_unit(x, filters, layers=3):\n",
        "    inp = x\n",
        "    for _ in range(layers):\n",
        "        x = conv(x, filters)\n",
        "    return add([x, inp])\n",
        "\n",
        "# 2D version of conv block\n",
        "def conv_block(x, filters, strides):\n",
        "    x = conv(x, filters)\n",
        "    x = residual_unit(x, filters)\n",
        "    if strides[0] > 1 or strides[1] > 1:\n",
        "        x = AveragePooling2D(pool_size=strides, strides=strides)(x)\n",
        "    return x\n",
        "\n",
        "# 2D version of build_model_A\n",
        "def build_model_E(input_shape, num_classes):\n",
        "    inp = Input(input_shape)\n",
        "    x = inp\n",
        "    x = conv_block(x, 16, (1, 4))\n",
        "    x = conv_block(x, 16, (1, 4))\n",
        "    x = conv_block(x, 32, (1, 4))\n",
        "    x = conv_block(x, 32, (1, 4))\n",
        "    x = Masking(mask_value=0)(x)\n",
        "\n",
        "    # Reshape for GRU\n",
        "    shape = x.shape\n",
        "    x = keras.layers.Reshape((shape[1] * shape[2], shape[3]))(x)\n",
        "\n",
        "    x = GRU(128, recurrent_dropout=0.1)(x)\n",
        "    x = Dense(num_classes, activation=\"softmax\")(x)\n",
        "\n",
        "    model = keras.models.Model(inp, x)\n",
        "    return model"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# == Initialize model ==\n",
        "\n",
        "# Explicitly specify the GPU device\n",
        "physical_devices = tf.config.experimental.list_physical_devices(\"GPU\")\n",
        "if len(physical_devices) > 0:\n",
        "    tf.config.experimental.set_memory_growth(physical_devices[0], True)\n",
        "\n",
        "input_shape = X_train_balanced.shape\n",
        "num_encoder_blocks = 8\n",
        "num_heads = 8 # 8\n",
        "key_dim = 25 # 25\n",
        "ff_dim = 4 # 24\n",
        "dropout = 0.5\n",
        "\n",
        "# Reshape\n",
        "X_train = X_train.reshape((-1, 2, 2000, 1))\n",
        "X_test = X_test.reshape((-1, 2, 2000, 1))\n",
        "X_train_balanced = X_train_balanced.reshape((-1, 2, 2000, 1))\n",
        "X_test_balanced = X_test_balanced.reshape((-1, 2, 2000, 1))\n",
        "\n",
        "# model = build_transformer_encoder_model(num_classes=NUM_CLASSES, input_shape=input_shape[1:], num_encoder_blocks=num_encoder_blocks, num_heads=num_heads, key_dim=key_dim, ff_dim=ff_dim, dropout=dropout)\n",
        "model = build_model_E(num_classes=NUM_CLASSES, input_shape=input_shape[1:])\n",
        "\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8yIfrPc8R27b",
        "outputId": "ced5d394-afcc-4f80-bd73-309c028db6f0"
      },
      "execution_count": 202,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Layer gru will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_1\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                Output Shape                 Param #   Connected to                  \n",
            "==================================================================================================\n",
            " input_8 (InputLayer)        [(None, 2, 2000, 1)]         0         []                            \n",
            "                                                                                                  \n",
            " conv2d_15 (Conv2D)          (None, 2, 2000, 16)          304       ['input_8[0][0]']             \n",
            "                                                                                                  \n",
            " batch_normalization_14 (Ba  (None, 2, 2000, 16)          64        ['conv2d_15[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " leaky_re_lu_1 (LeakyReLU)   (None, 2, 2000, 16)          0         ['batch_normalization_14[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " spatial_dropout2d_13 (Spat  (None, 2, 2000, 16)          0         ['leaky_re_lu_1[0][0]']       \n",
            " ialDropout2D)                                                                                    \n",
            "                                                                                                  \n",
            " conv2d_16 (Conv2D)          (None, 2, 2000, 16)          4624      ['spatial_dropout2d_13[0][0]']\n",
            "                                                                                                  \n",
            " batch_normalization_15 (Ba  (None, 2, 2000, 16)          64        ['conv2d_16[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " leaky_re_lu_2 (LeakyReLU)   (None, 2, 2000, 16)          0         ['batch_normalization_15[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " spatial_dropout2d_14 (Spat  (None, 2, 2000, 16)          0         ['leaky_re_lu_2[0][0]']       \n",
            " ialDropout2D)                                                                                    \n",
            "                                                                                                  \n",
            " conv2d_17 (Conv2D)          (None, 2, 2000, 16)          4624      ['spatial_dropout2d_14[0][0]']\n",
            "                                                                                                  \n",
            " batch_normalization_16 (Ba  (None, 2, 2000, 16)          64        ['conv2d_17[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " leaky_re_lu_3 (LeakyReLU)   (None, 2, 2000, 16)          0         ['batch_normalization_16[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " spatial_dropout2d_15 (Spat  (None, 2, 2000, 16)          0         ['leaky_re_lu_3[0][0]']       \n",
            " ialDropout2D)                                                                                    \n",
            "                                                                                                  \n",
            " conv2d_18 (Conv2D)          (None, 2, 2000, 16)          4624      ['spatial_dropout2d_15[0][0]']\n",
            "                                                                                                  \n",
            " batch_normalization_17 (Ba  (None, 2, 2000, 16)          64        ['conv2d_18[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " leaky_re_lu_4 (LeakyReLU)   (None, 2, 2000, 16)          0         ['batch_normalization_17[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " spatial_dropout2d_16 (Spat  (None, 2, 2000, 16)          0         ['leaky_re_lu_4[0][0]']       \n",
            " ialDropout2D)                                                                                    \n",
            "                                                                                                  \n",
            " add (Add)                   (None, 2, 2000, 16)          0         ['spatial_dropout2d_16[0][0]',\n",
            "                                                                     'spatial_dropout2d_13[0][0]']\n",
            "                                                                                                  \n",
            " average_pooling2d_12 (Aver  (None, 2, 500, 16)           0         ['add[0][0]']                 \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " conv2d_19 (Conv2D)          (None, 2, 500, 16)           4624      ['average_pooling2d_12[0][0]']\n",
            "                                                                                                  \n",
            " batch_normalization_18 (Ba  (None, 2, 500, 16)           64        ['conv2d_19[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " leaky_re_lu_5 (LeakyReLU)   (None, 2, 500, 16)           0         ['batch_normalization_18[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " spatial_dropout2d_17 (Spat  (None, 2, 500, 16)           0         ['leaky_re_lu_5[0][0]']       \n",
            " ialDropout2D)                                                                                    \n",
            "                                                                                                  \n",
            " conv2d_20 (Conv2D)          (None, 2, 500, 16)           4624      ['spatial_dropout2d_17[0][0]']\n",
            "                                                                                                  \n",
            " batch_normalization_19 (Ba  (None, 2, 500, 16)           64        ['conv2d_20[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " leaky_re_lu_6 (LeakyReLU)   (None, 2, 500, 16)           0         ['batch_normalization_19[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " spatial_dropout2d_18 (Spat  (None, 2, 500, 16)           0         ['leaky_re_lu_6[0][0]']       \n",
            " ialDropout2D)                                                                                    \n",
            "                                                                                                  \n",
            " conv2d_21 (Conv2D)          (None, 2, 500, 16)           4624      ['spatial_dropout2d_18[0][0]']\n",
            "                                                                                                  \n",
            " batch_normalization_20 (Ba  (None, 2, 500, 16)           64        ['conv2d_21[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " leaky_re_lu_7 (LeakyReLU)   (None, 2, 500, 16)           0         ['batch_normalization_20[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " spatial_dropout2d_19 (Spat  (None, 2, 500, 16)           0         ['leaky_re_lu_7[0][0]']       \n",
            " ialDropout2D)                                                                                    \n",
            "                                                                                                  \n",
            " conv2d_22 (Conv2D)          (None, 2, 500, 16)           4624      ['spatial_dropout2d_19[0][0]']\n",
            "                                                                                                  \n",
            " batch_normalization_21 (Ba  (None, 2, 500, 16)           64        ['conv2d_22[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " leaky_re_lu_8 (LeakyReLU)   (None, 2, 500, 16)           0         ['batch_normalization_21[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " spatial_dropout2d_20 (Spat  (None, 2, 500, 16)           0         ['leaky_re_lu_8[0][0]']       \n",
            " ialDropout2D)                                                                                    \n",
            "                                                                                                  \n",
            " add_1 (Add)                 (None, 2, 500, 16)           0         ['spatial_dropout2d_20[0][0]',\n",
            "                                                                     'spatial_dropout2d_17[0][0]']\n",
            "                                                                                                  \n",
            " average_pooling2d_13 (Aver  (None, 2, 125, 16)           0         ['add_1[0][0]']               \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " conv2d_23 (Conv2D)          (None, 2, 125, 32)           9248      ['average_pooling2d_13[0][0]']\n",
            "                                                                                                  \n",
            " batch_normalization_22 (Ba  (None, 2, 125, 32)           128       ['conv2d_23[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " leaky_re_lu_9 (LeakyReLU)   (None, 2, 125, 32)           0         ['batch_normalization_22[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " spatial_dropout2d_21 (Spat  (None, 2, 125, 32)           0         ['leaky_re_lu_9[0][0]']       \n",
            " ialDropout2D)                                                                                    \n",
            "                                                                                                  \n",
            " conv2d_24 (Conv2D)          (None, 2, 125, 32)           18464     ['spatial_dropout2d_21[0][0]']\n",
            "                                                                                                  \n",
            " batch_normalization_23 (Ba  (None, 2, 125, 32)           128       ['conv2d_24[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " leaky_re_lu_10 (LeakyReLU)  (None, 2, 125, 32)           0         ['batch_normalization_23[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " spatial_dropout2d_22 (Spat  (None, 2, 125, 32)           0         ['leaky_re_lu_10[0][0]']      \n",
            " ialDropout2D)                                                                                    \n",
            "                                                                                                  \n",
            " conv2d_25 (Conv2D)          (None, 2, 125, 32)           18464     ['spatial_dropout2d_22[0][0]']\n",
            "                                                                                                  \n",
            " batch_normalization_24 (Ba  (None, 2, 125, 32)           128       ['conv2d_25[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " leaky_re_lu_11 (LeakyReLU)  (None, 2, 125, 32)           0         ['batch_normalization_24[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " spatial_dropout2d_23 (Spat  (None, 2, 125, 32)           0         ['leaky_re_lu_11[0][0]']      \n",
            " ialDropout2D)                                                                                    \n",
            "                                                                                                  \n",
            " conv2d_26 (Conv2D)          (None, 2, 125, 32)           18464     ['spatial_dropout2d_23[0][0]']\n",
            "                                                                                                  \n",
            " batch_normalization_25 (Ba  (None, 2, 125, 32)           128       ['conv2d_26[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " leaky_re_lu_12 (LeakyReLU)  (None, 2, 125, 32)           0         ['batch_normalization_25[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " spatial_dropout2d_24 (Spat  (None, 2, 125, 32)           0         ['leaky_re_lu_12[0][0]']      \n",
            " ialDropout2D)                                                                                    \n",
            "                                                                                                  \n",
            " add_2 (Add)                 (None, 2, 125, 32)           0         ['spatial_dropout2d_24[0][0]',\n",
            "                                                                     'spatial_dropout2d_21[0][0]']\n",
            "                                                                                                  \n",
            " average_pooling2d_14 (Aver  (None, 2, 31, 32)            0         ['add_2[0][0]']               \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " conv2d_27 (Conv2D)          (None, 2, 31, 32)            18464     ['average_pooling2d_14[0][0]']\n",
            "                                                                                                  \n",
            " batch_normalization_26 (Ba  (None, 2, 31, 32)            128       ['conv2d_27[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " leaky_re_lu_13 (LeakyReLU)  (None, 2, 31, 32)            0         ['batch_normalization_26[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " spatial_dropout2d_25 (Spat  (None, 2, 31, 32)            0         ['leaky_re_lu_13[0][0]']      \n",
            " ialDropout2D)                                                                                    \n",
            "                                                                                                  \n",
            " conv2d_28 (Conv2D)          (None, 2, 31, 32)            18464     ['spatial_dropout2d_25[0][0]']\n",
            "                                                                                                  \n",
            " batch_normalization_27 (Ba  (None, 2, 31, 32)            128       ['conv2d_28[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " leaky_re_lu_14 (LeakyReLU)  (None, 2, 31, 32)            0         ['batch_normalization_27[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " spatial_dropout2d_26 (Spat  (None, 2, 31, 32)            0         ['leaky_re_lu_14[0][0]']      \n",
            " ialDropout2D)                                                                                    \n",
            "                                                                                                  \n",
            " conv2d_29 (Conv2D)          (None, 2, 31, 32)            18464     ['spatial_dropout2d_26[0][0]']\n",
            "                                                                                                  \n",
            " batch_normalization_28 (Ba  (None, 2, 31, 32)            128       ['conv2d_29[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " leaky_re_lu_15 (LeakyReLU)  (None, 2, 31, 32)            0         ['batch_normalization_28[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " spatial_dropout2d_27 (Spat  (None, 2, 31, 32)            0         ['leaky_re_lu_15[0][0]']      \n",
            " ialDropout2D)                                                                                    \n",
            "                                                                                                  \n",
            " conv2d_30 (Conv2D)          (None, 2, 31, 32)            18464     ['spatial_dropout2d_27[0][0]']\n",
            "                                                                                                  \n",
            " batch_normalization_29 (Ba  (None, 2, 31, 32)            128       ['conv2d_30[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " leaky_re_lu_16 (LeakyReLU)  (None, 2, 31, 32)            0         ['batch_normalization_29[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " spatial_dropout2d_28 (Spat  (None, 2, 31, 32)            0         ['leaky_re_lu_16[0][0]']      \n",
            " ialDropout2D)                                                                                    \n",
            "                                                                                                  \n",
            " add_3 (Add)                 (None, 2, 31, 32)            0         ['spatial_dropout2d_28[0][0]',\n",
            "                                                                     'spatial_dropout2d_25[0][0]']\n",
            "                                                                                                  \n",
            " average_pooling2d_15 (Aver  (None, 2, 7, 32)             0         ['add_3[0][0]']               \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " masking_6 (Masking)         (None, 2, 7, 32)             0         ['average_pooling2d_15[0][0]']\n",
            "                                                                                                  \n",
            " reshape (Reshape)           (None, 14, 32)               0         ['masking_6[0][0]']           \n",
            "                                                                                                  \n",
            " gru (GRU)                   (None, 128)                  62208     ['reshape[0][0]']             \n",
            "                                                                                                  \n",
            " dense_1 (Dense)             (None, 26)                   3354      ['gru[0][0]']                 \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 238266 (930.73 KB)\n",
            "Trainable params: 237498 (927.73 KB)\n",
            "Non-trainable params: 768 (3.00 KB)\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 203,
      "metadata": {
        "id": "Z3jbMs35jnOL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "81a4c1c1-7be3-4bd0-871a-0a801365ab82"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of GPUs available: 1\n",
            "Number of training data examples: 52000\n",
            "Number of classes: 26\n",
            "Shape of training data: (52000, 2, 2000, 1)\n",
            "Epochs: 50\n",
            "Learning rate: 0.001\n",
            "Batch size: 100\n",
            "Epoch 1/50\n",
            "390/390 [==============================] - 53s 101ms/step - loss: 2.9698 - sparse_categorical_accuracy: 0.1204 - val_loss: 3.0421 - val_sparse_categorical_accuracy: 0.1138 - lr: 0.0010\n",
            "Epoch 2/50\n",
            "390/390 [==============================] - 40s 101ms/step - loss: 2.7214 - sparse_categorical_accuracy: 0.2014 - val_loss: 2.6415 - val_sparse_categorical_accuracy: 0.2218 - lr: 0.0010\n",
            "Epoch 3/50\n",
            "390/390 [==============================] - 39s 100ms/step - loss: 2.6073 - sparse_categorical_accuracy: 0.2363 - val_loss: 2.6314 - val_sparse_categorical_accuracy: 0.2305 - lr: 0.0010\n",
            "Epoch 4/50\n",
            "390/390 [==============================] - 39s 100ms/step - loss: 2.5128 - sparse_categorical_accuracy: 0.2636 - val_loss: 2.4281 - val_sparse_categorical_accuracy: 0.2814 - lr: 0.0010\n",
            "Epoch 5/50\n",
            "390/390 [==============================] - 39s 101ms/step - loss: 2.4428 - sparse_categorical_accuracy: 0.2817 - val_loss: 2.3518 - val_sparse_categorical_accuracy: 0.3060 - lr: 0.0010\n",
            "Epoch 6/50\n",
            "390/390 [==============================] - 39s 100ms/step - loss: 2.3856 - sparse_categorical_accuracy: 0.2973 - val_loss: 2.2966 - val_sparse_categorical_accuracy: 0.3210 - lr: 0.0010\n",
            "Epoch 7/50\n",
            "390/390 [==============================] - 39s 100ms/step - loss: 2.3269 - sparse_categorical_accuracy: 0.3113 - val_loss: 2.2174 - val_sparse_categorical_accuracy: 0.3466 - lr: 0.0010\n",
            "Epoch 8/50\n",
            "390/390 [==============================] - 39s 100ms/step - loss: 2.2818 - sparse_categorical_accuracy: 0.3218 - val_loss: 2.2361 - val_sparse_categorical_accuracy: 0.3402 - lr: 0.0010\n",
            "Epoch 9/50\n",
            "390/390 [==============================] - 39s 101ms/step - loss: 2.2457 - sparse_categorical_accuracy: 0.3342 - val_loss: 2.1552 - val_sparse_categorical_accuracy: 0.3601 - lr: 0.0010\n",
            "Epoch 10/50\n",
            "390/390 [==============================] - 39s 101ms/step - loss: 2.2041 - sparse_categorical_accuracy: 0.3445 - val_loss: 2.1388 - val_sparse_categorical_accuracy: 0.3671 - lr: 0.0010\n",
            "Epoch 11/50\n",
            "390/390 [==============================] - 39s 100ms/step - loss: 2.1750 - sparse_categorical_accuracy: 0.3466 - val_loss: 2.0975 - val_sparse_categorical_accuracy: 0.3718 - lr: 0.0010\n",
            "Epoch 12/50\n",
            "390/390 [==============================] - 39s 100ms/step - loss: 2.1435 - sparse_categorical_accuracy: 0.3607 - val_loss: 2.0673 - val_sparse_categorical_accuracy: 0.3830 - lr: 0.0010\n",
            "Epoch 13/50\n",
            "390/390 [==============================] - 39s 100ms/step - loss: 2.1171 - sparse_categorical_accuracy: 0.3661 - val_loss: 2.0757 - val_sparse_categorical_accuracy: 0.3823 - lr: 0.0010\n",
            "Epoch 14/50\n",
            "390/390 [==============================] - 39s 101ms/step - loss: 2.0984 - sparse_categorical_accuracy: 0.3704 - val_loss: 2.0300 - val_sparse_categorical_accuracy: 0.3904 - lr: 0.0010\n",
            "Epoch 15/50\n",
            "390/390 [==============================] - 39s 100ms/step - loss: 2.0691 - sparse_categorical_accuracy: 0.3786 - val_loss: 2.0154 - val_sparse_categorical_accuracy: 0.3985 - lr: 0.0010\n",
            "Epoch 16/50\n",
            "390/390 [==============================] - 39s 100ms/step - loss: 2.0461 - sparse_categorical_accuracy: 0.3854 - val_loss: 2.0208 - val_sparse_categorical_accuracy: 0.3946 - lr: 0.0010\n",
            "Epoch 17/50\n",
            "390/390 [==============================] - 39s 101ms/step - loss: 2.0330 - sparse_categorical_accuracy: 0.3878 - val_loss: 2.0116 - val_sparse_categorical_accuracy: 0.3953 - lr: 0.0010\n",
            "Epoch 18/50\n",
            "390/390 [==============================] - 39s 101ms/step - loss: 2.0080 - sparse_categorical_accuracy: 0.3952 - val_loss: 1.9750 - val_sparse_categorical_accuracy: 0.4063 - lr: 0.0010\n",
            "Epoch 19/50\n",
            "390/390 [==============================] - 39s 101ms/step - loss: 1.9869 - sparse_categorical_accuracy: 0.4021 - val_loss: 1.9694 - val_sparse_categorical_accuracy: 0.4077 - lr: 0.0010\n",
            "Epoch 20/50\n",
            "390/390 [==============================] - 39s 100ms/step - loss: 1.9680 - sparse_categorical_accuracy: 0.4062 - val_loss: 1.9370 - val_sparse_categorical_accuracy: 0.4186 - lr: 0.0010\n",
            "Epoch 21/50\n",
            "390/390 [==============================] - 39s 100ms/step - loss: 1.9542 - sparse_categorical_accuracy: 0.4097 - val_loss: 1.9307 - val_sparse_categorical_accuracy: 0.4198 - lr: 0.0010\n",
            "Epoch 22/50\n",
            "390/390 [==============================] - 39s 101ms/step - loss: 1.9284 - sparse_categorical_accuracy: 0.4169 - val_loss: 1.9291 - val_sparse_categorical_accuracy: 0.4183 - lr: 0.0010\n",
            "Epoch 23/50\n",
            "390/390 [==============================] - 39s 101ms/step - loss: 1.9229 - sparse_categorical_accuracy: 0.4184 - val_loss: 1.9043 - val_sparse_categorical_accuracy: 0.4247 - lr: 0.0010\n",
            "Epoch 24/50\n",
            "390/390 [==============================] - 39s 100ms/step - loss: 1.9067 - sparse_categorical_accuracy: 0.4217 - val_loss: 1.8946 - val_sparse_categorical_accuracy: 0.4315 - lr: 0.0010\n",
            "Epoch 25/50\n",
            "390/390 [==============================] - 39s 101ms/step - loss: 1.8927 - sparse_categorical_accuracy: 0.4262 - val_loss: 1.8798 - val_sparse_categorical_accuracy: 0.4365 - lr: 0.0010\n",
            "Epoch 26/50\n",
            "390/390 [==============================] - 39s 101ms/step - loss: 1.8808 - sparse_categorical_accuracy: 0.4298 - val_loss: 1.8736 - val_sparse_categorical_accuracy: 0.4392 - lr: 0.0010\n",
            "Epoch 27/50\n",
            "390/390 [==============================] - 39s 101ms/step - loss: 1.8633 - sparse_categorical_accuracy: 0.4310 - val_loss: 1.8687 - val_sparse_categorical_accuracy: 0.4398 - lr: 0.0010\n",
            "Epoch 28/50\n",
            "390/390 [==============================] - 39s 101ms/step - loss: 1.8490 - sparse_categorical_accuracy: 0.4387 - val_loss: 1.8648 - val_sparse_categorical_accuracy: 0.4432 - lr: 0.0010\n",
            "Epoch 29/50\n",
            "390/390 [==============================] - 39s 101ms/step - loss: 1.8387 - sparse_categorical_accuracy: 0.4410 - val_loss: 1.8533 - val_sparse_categorical_accuracy: 0.4392 - lr: 0.0010\n",
            "Epoch 30/50\n",
            "390/390 [==============================] - 39s 101ms/step - loss: 1.8273 - sparse_categorical_accuracy: 0.4459 - val_loss: 1.8374 - val_sparse_categorical_accuracy: 0.4487 - lr: 0.0010\n",
            "Epoch 31/50\n",
            "390/390 [==============================] - 39s 100ms/step - loss: 1.8151 - sparse_categorical_accuracy: 0.4446 - val_loss: 1.8442 - val_sparse_categorical_accuracy: 0.4457 - lr: 0.0010\n",
            "Epoch 32/50\n",
            "390/390 [==============================] - 39s 100ms/step - loss: 1.7980 - sparse_categorical_accuracy: 0.4513 - val_loss: 1.8359 - val_sparse_categorical_accuracy: 0.4497 - lr: 0.0010\n",
            "Epoch 33/50\n",
            "390/390 [==============================] - 39s 100ms/step - loss: 1.7953 - sparse_categorical_accuracy: 0.4527 - val_loss: 1.8481 - val_sparse_categorical_accuracy: 0.4419 - lr: 0.0010\n",
            "Epoch 34/50\n",
            "390/390 [==============================] - 39s 100ms/step - loss: 1.7883 - sparse_categorical_accuracy: 0.4547 - val_loss: 1.8518 - val_sparse_categorical_accuracy: 0.4408 - lr: 0.0010\n",
            "Epoch 35/50\n",
            "390/390 [==============================] - 39s 100ms/step - loss: 1.7783 - sparse_categorical_accuracy: 0.4584 - val_loss: 1.8144 - val_sparse_categorical_accuracy: 0.4539 - lr: 0.0010\n",
            "Epoch 36/50\n",
            "390/390 [==============================] - 39s 101ms/step - loss: 1.7643 - sparse_categorical_accuracy: 0.4603 - val_loss: 1.8016 - val_sparse_categorical_accuracy: 0.4597 - lr: 0.0010\n",
            "Epoch 37/50\n",
            "390/390 [==============================] - 39s 100ms/step - loss: 1.7562 - sparse_categorical_accuracy: 0.4616 - val_loss: 1.8117 - val_sparse_categorical_accuracy: 0.4578 - lr: 0.0010\n",
            "Epoch 38/50\n",
            "390/390 [==============================] - 39s 100ms/step - loss: 1.7423 - sparse_categorical_accuracy: 0.4665 - val_loss: 1.8102 - val_sparse_categorical_accuracy: 0.4615 - lr: 0.0010\n",
            "Epoch 39/50\n",
            "390/390 [==============================] - 39s 101ms/step - loss: 1.7391 - sparse_categorical_accuracy: 0.4681 - val_loss: 1.8014 - val_sparse_categorical_accuracy: 0.4664 - lr: 0.0010\n",
            "Epoch 40/50\n",
            "390/390 [==============================] - 39s 101ms/step - loss: 1.7283 - sparse_categorical_accuracy: 0.4693 - val_loss: 1.7857 - val_sparse_categorical_accuracy: 0.4707 - lr: 0.0010\n",
            "Epoch 41/50\n",
            "390/390 [==============================] - 39s 100ms/step - loss: 1.7152 - sparse_categorical_accuracy: 0.4745 - val_loss: 1.7858 - val_sparse_categorical_accuracy: 0.4679 - lr: 0.0010\n",
            "Epoch 42/50\n",
            "390/390 [==============================] - 39s 100ms/step - loss: 1.7133 - sparse_categorical_accuracy: 0.4766 - val_loss: 1.8119 - val_sparse_categorical_accuracy: 0.4622 - lr: 0.0010\n",
            "Epoch 43/50\n",
            "390/390 [==============================] - 39s 101ms/step - loss: 1.7087 - sparse_categorical_accuracy: 0.4729 - val_loss: 1.7733 - val_sparse_categorical_accuracy: 0.4777 - lr: 0.0010\n",
            "Epoch 44/50\n",
            "390/390 [==============================] - 39s 100ms/step - loss: 1.6960 - sparse_categorical_accuracy: 0.4792 - val_loss: 1.7908 - val_sparse_categorical_accuracy: 0.4640 - lr: 0.0010\n",
            "Epoch 45/50\n",
            "390/390 [==============================] - 39s 101ms/step - loss: 1.6850 - sparse_categorical_accuracy: 0.4835 - val_loss: 1.7699 - val_sparse_categorical_accuracy: 0.4708 - lr: 0.0010\n",
            "Epoch 46/50\n",
            "390/390 [==============================] - 39s 101ms/step - loss: 1.6816 - sparse_categorical_accuracy: 0.4823 - val_loss: 1.7623 - val_sparse_categorical_accuracy: 0.4758 - lr: 0.0010\n",
            "Epoch 47/50\n",
            "390/390 [==============================] - 39s 100ms/step - loss: 1.6799 - sparse_categorical_accuracy: 0.4834 - val_loss: 1.7759 - val_sparse_categorical_accuracy: 0.4715 - lr: 0.0010\n",
            "Epoch 48/50\n",
            "390/390 [==============================] - 39s 101ms/step - loss: 1.6710 - sparse_categorical_accuracy: 0.4881 - val_loss: 1.7460 - val_sparse_categorical_accuracy: 0.4799 - lr: 0.0010\n",
            "Epoch 49/50\n",
            "390/390 [==============================] - 39s 100ms/step - loss: 1.6617 - sparse_categorical_accuracy: 0.4873 - val_loss: 1.7642 - val_sparse_categorical_accuracy: 0.4748 - lr: 0.0010\n",
            "Epoch 50/50\n",
            "390/390 [==============================] - 39s 100ms/step - loss: 1.6497 - sparse_categorical_accuracy: 0.4926 - val_loss: 1.7622 - val_sparse_categorical_accuracy: 0.4780 - lr: 0.0010\n"
          ]
        }
      ],
      "source": [
        "# == Train model ==\n",
        "\n",
        "EPOCHS = 50\n",
        "\n",
        "# Check if a GPU is available\n",
        "print(\"Number of GPUs available:\", len(tf.config.experimental.list_physical_devices(\"GPU\")))\n",
        "print(f\"Number of training data examples: {len(X_train_balanced)}\")\n",
        "print(f\"Number of classes: {NUM_CLASSES}\")\n",
        "print(f\"Shape of training data: {input_shape}\")\n",
        "print(f\"Epochs: {EPOCHS}\")\n",
        "print(f\"Learning rate: {LEARNING_RATE}\")\n",
        "print(f\"Batch size: {BATCH_SIZE}\")\n",
        "\n",
        "callbacks = [\n",
        "    keras.callbacks.ModelCheckpoint(f\"Model_{NUM_CLASSES}classes.h5\", save_best_only=True, monitor=\"val_loss\"),\n",
        "    keras.callbacks.ReduceLROnPlateau(monitor=\"val_loss\", factor=0.5, patience=3, min_lr=0.000005),\n",
        "    keras.callbacks.EarlyStopping(monitor=\"val_loss\", patience=10, verbose=1),\n",
        "]\n",
        "\n",
        "# optimizer = Adam(learning_rate=LEARNING_RATE)\n",
        "optimizer = Adam(learning_rate=LEARNING_RATE)\n",
        "\n",
        "\n",
        "# loss=\"binary_crossentropy\", metrics=[\"binary_accuracy\"]; sparse_categorical_crossentropy\n",
        "model.compile(optimizer=optimizer, loss=\"sparse_categorical_crossentropy\", metrics=[\"sparse_categorical_accuracy\"])\n",
        "history = model.fit(X_train_balanced, Y_train_balanced, batch_size=BATCH_SIZE, epochs=EPOCHS, callbacks=callbacks, validation_split=0.25, verbose=1)\n",
        "\n",
        "train_accuracy = history.history[\"sparse_categorical_accuracy\"] # list\n",
        "val_accuracy = history.history[\"val_sparse_categorical_accuracy\"] # list\n",
        "train_loss = history.history[\"loss\"] # list\n",
        "val_loss = history.history[\"val_loss\"] # list"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# == Plot training curve ==\n",
        "\n",
        "# Accuracy\n",
        "\n",
        "train_accuracy = train_accuracy\n",
        "val_accuracy = val_accuracy\n",
        "epochs_range = range(1, len(train_accuracy) + 1)\n",
        "plt.figure(figsize=(12, 6))\n",
        "plt.plot(epochs_range, train_accuracy, \"r\", label=\"Training Accuracy\")\n",
        "plt.plot(epochs_range, val_accuracy, \"b\", label=\"Validation Accuracy\")\n",
        "plt.title(\"Training and Validation Accuracy\")\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.legend()\n",
        "plt.savefig(\n",
        "    \"Training_accuracy_\"+str(NUM_CLASSES)+\"classes.png\",\n",
        "    dpi=300,\n",
        "    format=\"png\",\n",
        "    bbox_inches=\"tight\",\n",
        ")\n",
        "plt.show()\n",
        "plt.close()\n",
        "\n",
        "# Loss\n",
        "\n",
        "train_loss = train_loss\n",
        "val_loss = val_loss\n",
        "epochs_range = range(1, len(train_loss) + 1)\n",
        "plt.figure(figsize=(12, 6))\n",
        "plt.plot(epochs_range, train_loss, \"r\", label=\"Training Loss\")\n",
        "plt.plot(epochs_range, val_loss, \"b\", label=\"Validation Loss\")\n",
        "plt.title(\"Training and Validation Loss\")\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.legend()\n",
        "plt.savefig(\n",
        "    \"Training_loss_\"+str(NUM_CLASSES)+\"classes.png\",\n",
        "    dpi=300,\n",
        "    format=\"png\",\n",
        "    bbox_inches=\"tight\",\n",
        ")\n",
        "plt.show()\n",
        "plt.close()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 909
        },
        "id": "Rtmwe0Z-qO2n",
        "outputId": "cd99ff02-6c16-4945-9665-419ac01a8154"
      },
      "execution_count": 204,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# == Test model on testset ==\n",
        "\n",
        "pred_prob = model.predict(X_test)\n",
        "pred = np.argmax(pred_prob, axis=-1)\n",
        "print(f\"Accuracy testset: {accuracy_score(Y_test, pred)}\")\n",
        "\n",
        "pred_prob_balanced = model.predict(X_test_balanced)\n",
        "pred_balanced = np.argmax(pred_prob_balanced, axis=-1)\n",
        "print(f\"Accuracy testset balanced: {accuracy_score(Y_test_balanced, pred_balanced)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QVMpBgwYqShB",
        "outputId": "61ad4bf2-d999-4035-83a1-c3c1b0c2b96f"
      },
      "execution_count": 205,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1166/1166 [==============================] - 9s 7ms/step\n",
            "Accuracy testset: 0.24063296231728576\n",
            "81/81 [==============================] - 1s 9ms/step\n",
            "Accuracy testset balanced: 0.2536090518923137\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# == Confusion matrix ==\n",
        "\n",
        "if NUM_CLASSES == 26:\n",
        "    labels = [\n",
        "        \"SR\",  # sinus rhythm\n",
        "        \"AF\",  # atrial fibrillation\n",
        "        \"AFL\",  # atrial flutter\n",
        "        \"PAC/SVPB\",  # premature atrial contraction / supraventricular premature beats\n",
        "        \"PVC/VPB\",  # premature ventricular contractions / ventricular premature beats\n",
        "        \"BBB\",  # bundle branch block\n",
        "        \"Brady\",  # bradycardia\n",
        "        \"CLBBB/LBBB\",  # complete left bundle branch block / left bundle branch block\n",
        "        \"CRBBB/RBBB\",  # complete right bundle branch block / right bundle branch block\n",
        "        \"IAVB\",  # 1st degree AV block\n",
        "        \"IRBBB\",  # incomplete right bundle branch block\n",
        "        \"LAD\",  # left axis deviation\n",
        "        \"LAnFB\",  # left anterior fascicular block\n",
        "        \"LQRSV\",  # low QRS voltages\n",
        "        \"NSIVCB\",  # nonspecific intraventricular conduction disorder\n",
        "        \"PR\",  # pacing rhythm\n",
        "        \"PRWP\",  # poor R wave progression\n",
        "        \"LPR\",  # prolonged PR interval\n",
        "        \"LQT\",  # prolonged QT interval\n",
        "        \"QAb\",  # Q wave abnormal\n",
        "        \"RAD\",  # right axis deviation\n",
        "        \"SA\",  # sinus arrhythmia\n",
        "        \"SB\",  # sinus bradycardia\n",
        "        \"STach\",  # sinus tachycardia\n",
        "        \"TAb\",  # T wave abnormal\n",
        "        \"TInv\"  # T wave inversion\n",
        "    ]\n",
        "else:\n",
        "    labels = [\n",
        "        \"SR\",  # sinus rhythm\n",
        "        \"AF\",  # atrial fibrillation\n",
        "        \"AFL\",  # atrial flutter\n",
        "        \"PAC/SVPB\",  # premature atrial contraction / supraventricular premature beats\n",
        "        \"PVC/VPB\",  # premature ventricular contractions / ventricular premature beats\n",
        "    ]\n",
        "\n",
        "plt.figure(figsize=(10, 8))\n",
        "cm = confusion_matrix(Y_test, pred)\n",
        "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=labels, yticklabels=labels)\n",
        "\n",
        "plt.title('Physionet 2021')\n",
        "plt.ylabel('Actual Label')\n",
        "plt.xlabel('Predicted Label')\n",
        "plt.savefig(f\"ConfusionMatrix_unbalanced_{NUM_CLASSES}classes.png\")\n",
        "plt.show()\n",
        "\n",
        "plt.figure(figsize=(10, 8))\n",
        "cm = confusion_matrix(Y_test_balanced, pred_balanced)\n",
        "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=labels, yticklabels=labels)\n",
        "\n",
        "plt.title('Physionet 2021')\n",
        "plt.ylabel('Actual Label')\n",
        "plt.xlabel('Predicted Label')\n",
        "plt.savefig(f\"ConfusionMatrix_balanced_{NUM_CLASSES}classes.png\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "QiCXS9oYq644",
        "outputId": "ae722c57-78cd-4b5a-da42-acfa0bac9973"
      },
      "execution_count": 206,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "machine_shape": "hm",
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}